Extending the Charge Carrier Recombination Lifetime by Octahedral Rotations in Ruddlesden-Popper Ba3Zr2S7 Perovskites.

Intentional distortions of [BX6] octahedra within perovskite structures have been recognized as a potent strategy for precise band gap adjustments and optimization of their photovoltaic properties, yet information regarding charge carrier dynamics linked to octahedral distortion under ambient conditions for chalcogenide perovskites remains limited. In this study, we utilize ab initio nonadiabatic molecular dynamics to explore the dynamics of photogenerated carriers in a representative two-dimensional Ba3Zr2S7 material in the Ruddlesden-Popper phase. The theoretical results highlight the influence of octahedral rotation on the materials' stability and carrier recombination lifetime of the system. Specifically, the octahedrally rotating P42/mnm phase exhibits a prolonged nonradiative carrier recombination lifetime attributed to the stabilized electron-phonon coupling. These findings offer valuable insights into the fundamental physical characteristics of imposed octahedral distortion and its potential for optimizing the optoelectronic performance of 2D Ruddlesden-Popper Ba-Zr-S chalcogenide materials.

Electronic Couplings versus Thermal Fluctuations in the Internal Conversion of Perylene Diimides: The Battle to Localize the Exciton.

Energy transfer processes among units of light-harvesting homo-oligomers impact the efficiency of these materials as components in organic optoelectronic devices such as solar cells. Perylene diimide (PDI), a prototypical dye, features exceptional light absorption and highly tunable optical and electronic properties. These properties can be modulated by varying the number of PDI units and linkers between them. Herein, atomistic nonadiabatic excited state molecular dynamics is used to explore the energy transfer during the internal conversion of acetylene and diacetylene bridged dimeric and trimeric PDIs. Our simulations reveal a significant impact of the bridge type on the transient exciton localization/delocalization between units of PDI dimers. After electronic relaxation, larger exciton delocalization occurs in the PDI dimer connected by the diacetylene bridge with respect to the one connected by the shorter acetylene bridge. These changes can be rationalized by the Frenkel exciton model. We outline a technique for deriving parameters for this model using inputs provided by nonadiabatic dynamics simulations. Frenkel exciton description reveals an interplay between the relative strengths of the diagonal and off-diagonal disorders. Moreover, atomistic simulations and the Frenkel exciton model of the PDI trimer systems corroborate in detail the localization properties of the exciton on the molecular units during the internal conversion to the lowest-energy excited state when the units become effectively decoupled. Overall, atomistic nonadiabatic simulations in combination with the Frenkel exciton model can serve as a predictive framework for analyzing and predicting desired exciton traps in PDI-based oligomers designed for organic electronics and photonic devices.

Influence of Material Properties on Surface Chemistry Induced Circular Dichroism in Halide Perovskite: Computational Insights.

The chirality transfer phenomenon is attractive for enhancing the optical functionality of nanomaterials by inducing sensitivity to the circular polarization states of photons. An underexplored aspect is how material properties of the achiral semiconductor impact the induced chiroptical signatures. Here we apply atomistic time-dependent density functional theory simulations to investigate the material properties that influence the chiroptical signatures of a lead halide perovskite nanocrystal with a chiral molecule bound to the surface. First, we find that both lattice disorder created by surface strain and halide substitution can increase the chiroptical response of the perovskite quantum dots by an order of magnitude. Both phenomena are attributed to a broadening of the density of the electronically excited states. Second, the intensity of the anisotropy spectra decreases with increasing dot size with a power law decay. Overall, these insights can be used to help guide experimental realization of highly resolvable polarized optical features in semiconducting nanomaterials.

Extreme Ultraviolet Reflection Spectroscopy of Lanthanides and Actinides Using a High Harmonic Generation Light Source.

Absorption spectroscopy probing transitions from shallow-core d and f orbitals in lanthanides and actinides reveals information about bonding and the electronic structure in compounds containing these elements. However, spectroscopy in this photon energy range is challenging because of the limited availability of light sources and extremely short penetration depths. In this work, we address these challenges using a tabletop extreme ultraviolet (XUV), ultrafast, laser-driven, high harmonic generation light source, which generates femtosecond pulses in the 40-140 eV range. We present reflection spectroscopy measurements at the N4,5 (i.e., predominantly 4d to 5f transitions) and O4,5 (i.e., 5d to 5f transitions) absorption edges on several lanthanide and uranium oxide crystals. We compare these results to density functional theory calculations to assign the electronic transitions and predict the spectra for other lanthanides. This work paves the way for laboratory-scale XUV absorption experiments for studying crystalline and molecular f-electron systems, with applications ranging from surface chemistry, photochemistry, and electronic or chemical structure determination to nuclear forensics.

WFOT: A Wave Function Overlap Tool between Single- and Multi-Reference Electronic Structure Methods for Spectroscopy Simulation.

We report the development of a novel diagnostic tool, named wave function overlap tool (WFOT), designed to evaluate the overlap between wave functions computed at single-reference [i.e., time-dependent density functional theory or configuration interaction singles (CIS)] and multireference (i.e., CASSCF/CASPT2) electronic structure levels of theory. It relies on truncating the single- and multireference WFs to CIS-like expansions spanning the same configurational space and maximizing the molecular orbital overlap by means of a unitary transformation. To demonstrate the functionality of the tool, we calculate the transient spectrum of acetylacetone by evaluating excited state absorption signals with multireference quality on top of single-reference on-the-fly dynamics simulations. Semiautomatic spectra generation is facilitated by interfacing the tool with the COBRAMM package, which also allows one to use WFOT with several quantum chemistry codes such as Gaussian, NWChem, and OpenMolcas. Other exciting possibilities for the utilization of the code beyond the simulation of transient absorption spectroscopy are eventually discussed.

Nanocrystal Assemblies: Current Advances and Open Problems.

We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.

Stabilization of Charge-Transfer Excited States in Biological Systems: A Computational Focus on the Special Pair in Photosystem II Reaction Centers.

Charge-transfer (CT) excited states play an important role in many biological processes. However, many computational approaches often inadequately address the equilibration effects of nuclear and environmental degrees of freedom on these states. One prominent example of systems in which CT states are of utmost importance is reaction centers (RC) in photosystems. Here we use a multiscale approach combined with time-dependent density functional theory to explore the lowest CT excited state of the special pair PD1-PD2 in the Photosystem II-RC of a cyanobacterium. We find that the nonequilibrium CT excited state resides near the Soret band, making an exciton the lowest-energy excited state. However, accounting for nuclear and state-specific dielectric equilibration along the CT potential energy surface (PES), the CT state PD1--PD2+ stabilizes energetically below the excitonic state. This underscores the crucial role of state-specific solvation in mapping the PES of CT states, as demonstrated in a simplified dimer model.

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.

Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment.

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

Machine Learning Framework for Modeling Exciton Polaritons in Molecular Materials.

A light-matter hybrid quasiparticle, called a polariton, is formed when molecules are strongly coupled to an optical cavity. Recent experiments have shown that polariton chemistry can manipulate chemical reactions. Polariton chemistry is a collective phenomenon, and its effects increase with the number of molecules in a cavity. However, simulating an ensemble of molecules in the excited state coupled to a cavity mode is theoretically and computationally challenging. Recent advances in machine learning (ML) techniques have shown promising capabilities in modeling ground-state chemical systems. This work presents a general protocol to predict excited-state properties, such as energies, transition dipoles, and nonadiabatic coupling vectors with the hierarchically interacting particle neural network. ML predictions are then applied to compute the potential energy surfaces and electronic spectra of a prototype azomethane molecule in the collective coupling scenario. These computational tools provide a much-needed framework to model and understand many molecules' emerging excited-state polariton chemistry.

Plasmon mediated coherent population oscillations in molecular aggregates.

The strong coherent coupling of quantum emitters to vacuum fluctuations of the light field offers opportunities for manipulating the optical and transport properties of nanomaterials, with potential applications ranging from ultrasensitive all-optical switching to creating polariton condensates. Often, ubiquitous decoherence processes at ambient conditions limit these couplings to such short time scales that the quantum dynamics of the interacting system remains elusive. Prominent examples are strongly coupled exciton-plasmon systems, which, so far, have mostly been investigated by linear optical spectroscopy. Here, we use ultrafast two-dimensional electronic spectroscopy to probe the quantum dynamics of J-aggregate excitons collectively coupled to the spatially structured plasmonic fields of a gold nanoslit array. We observe rich coherent Rabi oscillation dynamics reflecting a plasmon-driven coherent exciton population transfer over mesoscopic distances at room temperature. This opens up new opportunities to manipulate the coherent transport of matter excitations by coupling to vacuum fields.

Dielectric Screening and Charge-Transfer in 2D Lead-Halide Perovskites for Reduced Exciton Binding Energies.

Layered lead-halide perovskites have shown tremendous success as an active material for optoelectronics. This is attributed to the electronic structure of the inorganic sublattice and large exciton binding energies due to quantum and dielectric confinement. Expanding functionalities for applications that depend on free-carrier generation requires new material design routes to decrease the binding energy. Here we use electronic structure methods with model Bethe-Salpeter equation (BSE) to examine the contributions of the dielectric screening and charge-transfer excited-states to the exciton binding energy of phenylethylammonium (PEA2PbBr4) and naphthlethylammonium (NEA2PbBr4) lead-bromide perovskites. Our model BSE calculations show that NEA introduces hole acceptor states which impose charge-transfer character on the exciton along with larger dielectric screening. This substantially decreases the exciton binding compared to PEA. This result suggests the use of organic cations with high dielectric screening and hole acceptor states as a viable strategy for reducing exciton binding energies in two-dimensional halide perovskites.

Quantum Simulation of Molecular Response Properties in the NISQ Era.

Accurate modeling of the response of molecular systems to an external electromagnetic field is challenging on classical computers, especially in the regime of strong electronic correlation. In this article, we develop a quantum linear response (qLR) theory to calculate molecular response properties on near-term quantum computers. Inspired by the recently developed variants of the quantum counterpart of equation of motion (qEOM) theory, the qLR formalism employs "killer condition" satisfying excitation operator manifolds that offer a number of theoretical advantages along with reduced quantum resource requirements. We also used the qEOM framework in this work to calculate the state-specific response properties. Further, through noiseless quantum simulations, we show that response properties calculated using the qLR approach are more accurate than the ones obtained from the classical coupled-cluster-based linear response models due to the improved quality of the ground-state wave function obtained using the ADAPT-VQE algorithm.

Benchmark Data Set of Crystalline Organic Semiconductors.

This work reports a Benchmark Data set of Crystalline Organic Semiconductors to test calculations of the structural and electronic properties of these materials in the solid state. The data set contains 67 crystals consisting of mostly rigid molecules with a single dominant conformer, covering the majority of known structural types. The experimental crystal structure is available for the entire data set, whereas zero-temperature unit cell volume can be reliably estimated for a subset of 28 crystals. Using this subset, we benchmark r2SCAN-D3 and PBE-D3 density functionals. Then, for the entire data set, we benchmark approximate density functional theory (DFT) methods, including GFN1-xTB and DFTB3(3ob-3-1), with various dispersion corrections against r2SCAN-D3. Our results show that r2SCAN-D3 geometries are accurate within a few percent, which is comparable to the statistical uncertainty of experimental data at a fixed temperature, but the unit cell volume is systematically underestimated by 2% on average. The several times faster PBE-D3 provides an unbiased estimate of the volume for all systems except for molecules with highly polar bonds, for which the volume is substantially overestimated in correlation with the underestimation of atomic charges. Considered approximate DFT methods are orders of magnitude faster and provide qualitatively correct but overcompressed crystal structures unless the dispersion corrections are fitted by unit cell volume.

Twisting Aromaticity and Photoinduced Dynamics in Hexapole Helicenes.

Curved aromatic molecules are attractive electronic materials, where an additional internal strain uniquely modifies their structure, aromaticity, dynamics, and optical properties. Helicenes are examples of such twisted conjugated systems. Herein, we analyze the photoinduced dynamics in different stereoisomers of a hexapole helicene by using nonadiabatic excited-state molecular dynamics simulations. We explore how changes in symmetry and structural distortion modulate the intramolecular energy redistribution. We find that distinct helical assembly leads to different rigid distorted structures that in turn impact the nonradiative energy relaxation and ultimately formation of the self-trapped exciton. Subsequently, the value of the twisting angles relative to the central triphenylene core structure controls the global molecular aromaticity and electronic localization during the internal conversion process. Our work sheds light on how the future synthesis of novel curved aromatic compounds can be directed to attain specific desired electronic properties through the modulation of their twisted aromaticity.

Composition Dependent Strain Engineering of Lead-Free Halide Double Perovskite: Computational Insights.

The critical photophysical properties of lead-free halide double perovskites (HDPs) must be substantially improved for various applications. In this regard, strain engineering is a powerful tool for enhancing optoelectronic performance with precise control. Here, we employ ab initio simulations to investigate the impact of mild compressive and tensile strains on the photophysics of Cs2AgB'X6 (B' = Sb, Bi; X = Cl, Br) perovskites. Depending on the pnictogen and halide atoms, the band gap and band edge positions of HDPs can be tuned to a significant extent by controlling the applied external strain. Cs2AgSbBr6 has the most substantial strain response under structural perturbations. The subtle electronic interactions among the participating orbitals and the band dispersion at the edge states are enhanced under compressive strain, reducing the carrier effective masses. The exciton binding energies for these Br-based HDPs are in the range 59-78 meV and weaken in the compressed lattices, suggesting improved free carrier generation. Overall, the study emphasizes the potential of lattice strain engineering to boost the photophysical properties of HDPs that can ultimately improve their optoelectronic performance.

X-ray and Optical Circular Dichroism as Local and Global Ultrafast Chiral Probes of [12]Helicene Racemization.

Chirality is a fundamental molecular property that plays a crucial role in biophysics and drug design. Optical circular dichroism (OCD) is a well-established chiral spectroscopic probe in the UV-visible regime. Chirality is most commonly associated with a localized chiral center. However, some compounds such as helicenes (Figure 1) are chiral due to their screwlike global structure. In these highly conjugated systems, some electric and magnetic allowed transitions are distributed across the entire molecule, and OCD thus probes the global molecular chirality. Recent advances in X-ray sources, in particular the control of their polarization and spatial profiles, have enabled X-ray circular dichroism (XCD), which, in contrast to OCD, can exploit the localized and element-specific nature of X-ray electronic transitions. XCD therefore is more sensitive to local structures, and the chirality probed with it can be referred to as local. During the racemization of helicene, between opposite helical structures, the screw handedness can flip locally, making the molecule globally achiral while retaining a local handedness. Here, we use the racemization mechanism of [12]helicene as a model to demonstrate the capabilities of OCD and XCD as time-dependent probes for global and local chiralities, respectively. Our simulations demonstrate that XCD provides an excellent spectroscopic probe for the time-dependent local chirality of molecules.

Synergy of semiempirical models and machine learning in computational chemistry.

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort-design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.

NWChem: Recent and Ongoing Developments.

This paper summarizes developments in the NWChem computational chemistry suite since the last major release (NWChem 7.0.0). Specifically, we focus on functionality, along with input blocks, that is accessible in the current stable release (NWChem 7.2.0) and in the "master" development branch, interfaces to quantum computing simulators, interfaces to external libraries, the NWChem github repository, and containerization of NWChem executable images. Some ongoing developments that will be available in the near future are also discussed.

Neural network atomistic potentials for global energy minima search in carbon clusters.

The global energy optimization problem is an acute and important problem in chemistry. It is crucial to know the geometry of the lowest energy isomer (global minimum, GM) of a given compound for the evaluation of its chemical and physical properties. This problem is especially relevant for atomic clusters. Due to the exponential growth of the number of local minima geometries with the increase of the number of atoms in the cluster, it is important to find a computationally efficient and reliable method to navigate the energy landscape and locate a true global minima structure. Newly developed neural network (NN) atomistic potentials offer a numerically efficient and relatively accurate approach for molecular structure optimization. An important question that needs to be answered is "Can NN potentials, trained on a given set, represent the potential energy surface (PES) of a neighboring domain?". In this work, we tested the applicability of ANI-1ccx and ANI-nr NN atomistic potentials for the global minima optimization of carbon clusters Cn (n = 3-10). We showed that with the introduction of the cluster connectivity restriction and consequent DFT or ab initio calculations, ANI-1ccx and ANI-nr can be considered as robust PES pre-samplers that can capture the GM structure even for large clusters such as C20.